Geographic Information System Research Articles

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Estimating groundwater resources in an equatorial forested basin using geographic information system and remote sensing techniques (case of Nyong, South Cameroon)

Estimating groundwater resources in an equatorial forested basin using geographic information system and remote sensing techniques (case of Nyong, South Cameroon)

A parameter to featuring the cultural landscape genes of traditional settlements in China: a perspective of geographical information

Creative transformation and innovative development of traditional cultures is a pressing social need in China because of the deep reform and rapid socio-economic developments. It is crucial to understand the natural features of cultural landscapes of traditional settlements because they are indispensable components of these heritages. Although there are significant findings on the cultural features of traditional settlements, people still face substantial difficulties in using Geographical Information Systems (GIS) to directly handle their cultural factors due to a lack of theoretical models or described methods. To address this issue, at first, this study analyzed the component elements and contents of Cultural Landscape Genes of Traditional Settlements (CLGTS) from the perspective of Geographical Information (GI). Then, the concept “Feature Parameter of Geographical Information of CLGTS (FPGI-CLGTS)” was proposed based on the above. Simultaneously, the connotations, identification rules, and mathematical meanings of FPGI-CLGTS were also explored deeply. Next, this study developed the identification methods of FPGI-CLGTS. Finally, this study examined FPGI-CLGTS through an experiment on the spatial layouts CLGTS of six samples in Hunan, China. Through this study, FPGI-CLGTS has enormous potential to reveal the critical cultural features and values of traditional settlements.

Trends in the mobility of primary healthcare human resources in underdeveloped regions of western China from 2000 to 2021: Evidence from Nanning

ObjectiveThis research aimed to identify the fundamental and geographic characteristics of the primary healthcare personnel mobility in Nanning from 2000 to 2021 and clarify the determinants that affect their transition to non-primary healthcare institutions.MethodsThrough utilizing the Primary Healthcare Personnel Database (PHPD) for 2000–2021, the study conducts descriptive statistical analysis on demographic, economic, and professional aspects of healthcare personnel mobility across healthcare reform phases. Geographic Information Systems (QGIS) were used to map mobility patterns, and R software was employed to calculate spatial autocorrelation (Moran’s I). Logistic regression identified factors that influenced the transition to non-primary institutions.ResultsPrimary healthcare personnel mobility is divided into four phases: initial (2000–2008), turning point (2009–2011), rapid development (2012–2020), and decline (2021). The rapid development stage saw increased mobility with no spatial clustering in inflow and outflow. From 2016 to 2020, primary healthcare worker mobility reached its peak, in which the most significant movement occurred between township health centers and other institutions. Aside from their transition to primary medical institutions, the primary movement of grassroots health personnel predominantly directs towards secondary general hospitals, tertiary general hospitals, and secondary specialized hospitals. Since 2012, the number and mobility distance of primary healthcare workers have become noticeably larger and remained at a higher level from 2016 to 2020. The main migration of primary healthcare personnel occurred in their districts (counties). Key transition factors include gender, education, ethnicity, professional category, general practice registration, and administrative division.ConclusionsThis study provides evidence of the features of primary healthcare personnel mobility in the less developed western regions of China, in which Nanning was taken as a case study. It uncovers the factors that impact the flow of primary healthcare personnel to non-primary healthcare institutions. These findings are helpful to policy refinement and support the retention of primary healthcare workers.

Scalable business models for startups in renewable energy: Strategies for using GIS technology to enhance SME scaling

The renewable energy sector has witnessed remarkable growth in recent years, driven by global efforts to mitigate climate change and transition towards sustainable energy sources. Startups play a crucial role in this transition, often pioneering innovative solutions and technologies. However, for these startups to thrive and contribute significantly to the renewable energy landscape, scalable business models are essential. This abstract explores strategies for leveraging Geographic Information System (GIS) technology to enhance scalability in small and medium-sized enterprise (SME) startups operating in the renewable energy sector. GIS technology offers a powerful framework for analyzing and visualizing spatial data, providing valuable insights into site selection, resource assessment, and infrastructure planning. By integrating GIS into their business models, startups can optimize operations, reduce costs, and accelerate growth. This review discusses various strategies for utilizing GIS technology, including market analysis, site suitability assessment, supply chain optimization, and infrastructure planning. Drawing on case studies and industry examples, this review highlights the effectiveness of GIS in enhancing scalability for renewable energy startups. It also addresses the challenges and limitations associated with GIS implementation, such as technical complexities, data accessibility, and cost considerations. Furthermore, this review discusses emerging trends in GIS technology and their potential impact on the renewable energy sector, offering insights into future opportunities for startups. In conclusion, this review emphasizes the importance of GIS technology in facilitating scalable business models for renewable energy startups. By harnessing the power of GIS, SMEs can unlock new growth opportunities, drive innovation, and contribute significantly to the transition towards a sustainable energy future. Keywords: Scalable Business Models, Startups, Renewable Energy, GIS Technology, SME Scaling.

Soil Erosion Risk Estimation by using Semi Empirical RUSLE model: A case study of Maniyari Basin, Chhattisgarh

Soil is the protective skin of our earth's surface, but today’s numerous population pressures on land, along with industrialization, climatic variability such as a vigorous increase in temperature, acid rain, and deforestation, definitely degrade the quality of land. It should have to evaluate the quality of the land and find out the nutrition status as well as the soil health. The present study is employed in a Geographic Information System (GIS) environment to predict erosion risk using the Semi-Empirical Revised Soil Loss Erosion Model (RUSLE). The physiographic soil map has been prepared by visual interpretation of the Sentinal 2 satellite image, from which the soil erodibility factor has been derived. The digital elevation model (DEM) has been prepared from a contour map and used as the base map for the topographic-related analysis. In this model, the slope length (LS) factor has been prepared from the DEM. The crop conservation and management factor (C) and support practice factor (P) factors have been derived from the LULC map. It has been found that 4.45% of the watershed comes under very high erosion, 3.50% under high erosion, 7.80% under moderate erosion, 11.37% under low erosion, and 51.36% under a very low erosion-prone zone.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices.  Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km².  Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor).  The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use.  Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region.  Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin.  Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

FOREST FIRES RISK IN A SEMI ARID REGION: A CASE STUDY IN WESTERN ALGERIA

Purpose: The objective of this study was to measure the fire risk of forests and to compare areas with high sensitivity to forest fires.   Theoretical Framework: Many authors have demonstrated the fragility of Algerian forests in the face of fires (Belhadj, 2003; Souidi et al., 2010 and 2017). In this research paper, the researchers focused on identifying the factors responsible for the risk of forest fires in Algeria using a state-of-the-art tool, the Geographic Information System (GIS). The researchers explained a scientific process for exploring new indicators using GIS, and showed how to develop a forest fire risk map.   Design/Methodology/Approach: The elaboration of the fire risk map requires the application of a model set up, and tested on forest areas in the Mediterranean region. The applied model involves the three main factors of forest fire risk assessment: topomorphology, fuel, and human occupation.   Findings: According to the results obtained, our study area is dominated by a very high risk index. In addition, more than half of the surface area of our study zone is located in topomorphological conditions that are highly conducive to the multiplication of fires and can increase the risk of forest fires breaking out. This study will help us to better control the risk of fires and to manage fire-fighting operations in the field.   Research, Practical & Social Implications: Mapping the risk of forest fires using GIS will help land managers and planners to identify sensitive areas for better preservation and intervention in the event of fire.   Originality/Value: The results show that the number of forest fires is increasing over time and that managing them is becoming increasingly difficult. The use of a GIS for all stakeholder and managers will make it possible to combat forest fires.

GIS based daily tourism route proposal for surrounding provinces and districts from Ankara’s Çankaya district (Turkey)

AbstractWith the dynamic evolution of the tourism sector, a multitude of shifts in tourism activities and traveler motivations have transpired. These changes, spurred by technological advancements, economic fluctuations, and geopolitical developments due to heightened global competition, have introduced a fresh dimension to tourism dynamics. Significantly, the strategic creation of new tourism itineraries has become pivotal, given the tourism sector's integral role in city branding. In this study, the focal point was the Çankaya district within the Ankara province, deemed a central reference. Positioned at the heart of Turkey, Ankara province boasts the second-largest population according to TUIK (2020) data. Within this province, Çankaya district stands as the most densely populated, rendering it the designated study area. In Çankaya, which is one of the most heavily employed areas by public workers in Turkey, strategically positioned and with high population potential, the tourism planning of day trips, especially on weekends, is crucial to be conducted within the framework of time–distance savings. Without such planning, it might be challenging to achieve an efficient travel organization, especially for tourists who allocate fewer resources to travel planning in recent years, have limited time, and are selective about tourism destinations. In line with the research problem, the aim of the study is to conduct a comprehensive analysis of tourism accessibility, using the example of Çankaya district in Ankara, and to visualize the results of the analysis within a quantitative methodology framework to present concrete outputs. The more specific objective of this study is to determine a tourism route that allows tourists to reach important tourist attractions in the shortest and most convenient routes within the framework of time–distance savings, with a maximum travel time of 3 h in the research area. The expected scientific contributions from the objectives are as follows: contributing to transportation optimization related to tourism, bringing attention to historical, natural, and cultural places within the determined tourism route that are still undiscovered, less known, or not evaluated as individual tourism destinations, providing valuable information to destination managers, raising awareness about tourism opportunities in each destination, and contributing economically to the region. Employing a quantitative methodology, the study hinged on time-based road matrix analysis, facilitated by Geographical Information Systems, to chart routes from Çankaya district to surrounding districts. During this analysis, preference was given to routes with gentle curves, mitigating negative impacts on vehicle speed compared to more convoluted alternatives. Subsequently, leveraging 21 distinct types of tourism attractions, tourism sites feasible for visits within 1, 2, and 3-h intervals were pinpointed. At the study's culmination, an assemblage of 841 daily tourist destinations in and around Ankara were meticulously delineated and cartographically depicted using MapInfo Pro 2019.3 version. This endeavor underscores a concerted effort to offer tourists, both local and beyond, a well-crafted roadmap to explore and relish the plethora of cultural, historical, and natural treasures that Ankara and its neighboring regions have to offer.

Cross-Sectional Study of Flood Damage Assumptions in Medical Facilities Using Geographic Information Systems

Cross-Sectional Study of Flood Damage Assumptions in Medical Facilities Using Geographic Information Systems

Legal Frameworks for the Assessment of Agricultural Lands

This study examines the legal frameworks governing the assessment of agricultural lands, focusing on the challenges and implications of existing regulations and suggesting improvements for more effective land management. Assessing agricultural land is crucial for determining its value, taxation, and eligibility for government subsidies, which directly impacts agricultural productivity and sustainability.The research methodology involves a comparative analysis of legislation in different jurisdictions, highlighting best practices and identifying gaps in current legal frameworks. Through this analysis, the study addresses how legal provisions align with the practical needs of agricultural development and land conservation, as well as discussing the impact of these laws on farmers, landowners, and the broader economic landscape.Key findings reveal that many existing laws are outdated and do not adequately accommodate modern agricultural practices or the challenges posed by climate change. The study also identifies a lack of consistency in the application of these laws, leading to disparities in land assessment and management across regions.Recommendations include integrating advanced geographic information systems (GIS) for more precise land valuation, adopting environmentally sustainable practices in legal criteria, and enhancing legal clarity to reduce administrative burdens on landowners.This research is intended for policymakers, legal experts, and stakeholders in the agricultural sector. It provides a comprehensive overview of the legal issues surrounding agricultural land assessment and proposes actionable solutions to enhance the effectiveness of these legal frameworks. By reforming agricultural land assessment laws, countries can better support their agricultural sectors, promoting economic stability and sustainable development.

Optimization of municipal solid waste collection system: systematic review with bibliometric literature analysis

AbstractMunicipal solid waste management (MSWM) requires significant planning and objective definitions of each of its stages. Waste collection and transportation stages are of utmost importance, and they represent a significant cost of the process. In this context, a systematic study using bibliometric analysis was conducted, seeking to identify and understand the applied methodologies and tools, as well as which parameters and approaches are employed to optimize the solid urban waste collection system. The analysis portfolio features 12 publications, focusing on the optimization of municipal solid waste (MSW) collection systems, mostly with the aim of optimizing routes for transporting waste from the collection points to the final destination, with the goal of reducing the costs of this stage. Results highlight how these studies only consider as optimization criteria the reduction of the travelled distance and lack other dimensions (such as an environmental impact perspective). Some of the studies demonstrate the vital role of technology in optimizing the waste collection operation, from the use of geographic information systems (GIS) to using sensors or smart bins. Future research efforts should widen the scope of what is considered in optimizing the waste collection system.

SISTEM INFORMASI GEOGRAFIS LAPANGAN PERTANDINGAN PORA KE XIV TAHUN 2022 DI KABUPATEN PIDIE BERBASIS QGIS

Pidie Regency is an area that provides many field facilities to implement. After conducting research directly by the authors, the authors found more than 30 fields to serve as PORA venues in Pidie District. However, not everyone knows the location of the field, including match information. This writing will discuss the creation of geographic information system (GIS) locations for Pidie Regency, especially the places or points where PORA XIV fields in Pidie Regency are increasing. The method used in this research is a geographic-based system or Global Information System (GIS). The aim of this research is how this system can display data from the field site in the form of maps or spatial data so that it can be more easily understood by users. Implementation of this system is made using the programming language used is PHP, HTML and CSS. Meanwhile, for processing the database, it uses the Mariadb database, which is a database capable of processing spatial data and attribute data. The result of this distribution analysis is a map of the playing fields in Pidie Regency.

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making

Background Antimicrobial Resistance (AMR) is a critical global public health concern, demanding effective tools for research, data analysis, and decision-making. This study proposes a groundbreaking approach to empower the global AMR research community by introducing interactive Geographic Information System (GIS) dashboards. These dashboards aim to facilitate comprehensive data analysis of AMR across multiple countries, providing insights into antimicrobial usage (AMU), resistance patterns, and geographic distribution. Methods The novel approach involves the development of GIS dashboards that integrate and harmonize data from diverse sources, including clinical laboratories, surveillance networks, and public health agencies. Objective 1 focuses on creating a dashboard encompassing all countries, offering comprehensive data analysis capabilities and visualization tools. Objective 2 entails building a focused dashboard specifically for Kenya and Uganda, allowing for comparative analysis of AMR in these regions. Objective 3 involves the generation of a simulated dataset for Kampala, Uganda, addressing data limitations in that specific area. Results The GIS dashboards serve as powerful tools for visualizing and analyzing AMR-related datasets, providing stakeholders with a comprehensive view of the global AMR landscape. These dashboards offer valuable insights into antimicrobial usage, resistance patterns, and geographical distribution. The centralized platform facilitates data exploration and analysis, aiding researchers, policymakers, and healthcare professionals in making informed decisions to combat AMR. Conclusions In conclusion, the study demonstrates that the developed GIS dashboards empower stakeholders by providing valuable insights and informed decision-making capabilities. The dashboards serve as essential tools for addressing the global challenge of AMR, allowing for a deeper understanding of the problem and informing effective strategies. The approach outlined in this study has the potential to significantly contribute to the ongoing efforts to combat AMR on a global scale.

Geographical distribution of nitrate pollution and its risk assessment using GIS and Monte Carlo simulation in drinking water in urban areas of Fars province-Iran during 2017-2021.

Pollution of water resources with nitrate is currently one of the major challenges at the global level. In order to make macro-policy decisions in water safety plans, it is necessary to carry out nitrate risk assessment in underground water, which has not been done in Fars province for all urban areas. In the current study, 9494 drinking water samples were collected in four seasons in 32 urban areas of Fars province in Iran, between 2017 and 2021 to investigate the non-carcinogenic health risk assessment. Geographical distribution maps of hazard quotient were drawn using geographical information system software. The results showed that the maximum amount of nitrate in water samples in 4% of the samples in 2021, 2.5% of the samples in 2020 and 3% of the samples in 2019 were more than the standard declared by World Health Organization guidelines (50mg/L). In these cases, the maximum amount of nitrate was reported between 82 and 123mg/L. The HQ values for infants did not exceed 1 in any year, but for children (44% ± 10.8), teenagers (10.8% ± 8.4), and adults (3.2% ± 1.7) exceeded 1 in cities, years, and seasons, indicating that three age groups in the studied area are at noticeably significant non-carcinogenic risk. The results of the Monte Carlo simulation showed that the average value of non-carcinogenic risk was less than 1 for all age groups. Moreover, the maximum HQ values (95%) were higher than 1 for both children and teenager, indicating a significant non-carcinogenic risk for the two age groups.

Epidemiological update of cystic echinococcosis in livestock and assessment of practices related to its control in the Mediterranean area.

Cystic echinococcosis (CE), caused by the tapeworm Echinococcus granulosus, is a zoonotic parasitic disease that still represents a serious threat to human and animal health worldwide. The Mediterranean basin is recognized as one of the major hotspots of CE due to several factors, including the presence of diverse intermediate host species as well as socio-economic and cultural conditions of local communities. This study aims to take a closer look at epidemiological data on CE in the Mediterranean area and assess the knowledge attitudes and practices of shepherds towards this disease in four countries (Algeria, Greece, Italy and Tunisia), highly endemic for CE, with the final goal of identifying highly endemic risk areas and practices in use which might potentially allow the persistence of E. granulosus infection in these areas. To update the epidemiological scenario of CE in Mediterranean areas, a comprehensive review of peer-reviewed literature on CE prevalence data published during the 2017-2023 period was carried out and, through a geographical information system (GIS), a map displaying the current CE distribution in the Mediterranean area was generated. In addition, a questionnaire survey was conducted through in-depth interviews of the farmers to collect information on their management system as well as knowledge attitudes and practices towards CE. From the farmer-participatory survey some risky practices emerged including the non-regular deworming of dogs or the use of ineffective drugs or dosing, as well as the provision of uncooked animal viscera to dogs. Finally, lower levels of knowledge and awareness of the disease was observed among farmers from North Africa compared with those of European countries. In conclusion, the results obtained highlight that CE is still a very serious problem in Mediterranean areas and increased efforts are needed to promote awareness among farmers and to turn research results into policy in order to reduce the spread of this disease, according to the One Health perspective.

Identification of appropriate sites for solar-based green hydrogen production using a combination of density-based clustering, Best-Worst Method, and Spatial GIS

Hydrogen is a relatively new source of sustainable energy resources; presently, its usage ranges from direct energy generation in mixed-cycle thermal power plants to its usage in the internal combustion engine. If the generation of hydrogen is coupled with renewable energy resources, the use of hydrocarbons can be substantially reduced in both energy generation and transportation. In this paper, we conducted a national-level assessment to identify the hydrogen generation capacity of Pakistan. The methodology used in this study is a combination of spatial multi-criteria analysis and density-based clustering in a geographical information systems environment. Based on the analysis, the national clusters capable of producing green hydrogen were identified. The results shows that the hydrogen generation capacity for Pakistan is approximately 7 million tons per year using solar photovoltaic energy. The Surab-Gwadar alignment, N-25 national highway, and Quetta-to-D-I-Khan alignment offer potential hydrogen generation sites with water resources. Most of the identified sites for the production of hydrogen in the country were found to be near the national electricity transmission networks. The Best-Worst Method was further employed to provide policymakers with a road map for prioritizing the development of these capacities based on their proximity to the national networks, their terrain suitability, and their generation capacities.

Geographic Information System Mapping Tool for Rainwater Harvesting in the United States

Geographic Information System Mapping Tool for Rainwater Harvesting in the United States